Revolutionize your underwriting strategy with cash flow data

Cash Atlas™ analyzes bank transaction data to enable you to generate a complete risk profile for consumers across the credit spectrum. In conjunction with traditional credit data, you can now make more informed decisions on no-file, thin-file, or thick-file consumers. Cash Atlas™ provides FCRA consumer reports to help you understand consumer affordability and likelihood to pay.

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Why Cash Atlas™

  • Improve risk assessment

    Cash flow data can be used in conjunction with or in lieu of traditional credit history to provide an enhanced view of your consumer’s affordability and likelihood to pay, both now and in the future.

  • Originate more loans

    By measuring affordability in conjunction with credit risk, Cash Atlas™ enables unparalleled insight into consumer creditworthiness to expand your market.

  • Reduce testing cycles while maximizing risk prediction

    Attributes proven to predict credit risk means you can spend less time testing and more time driving new consumer growth.

For many industries

We empower banks, credit unions, fintechs, and lenders to make informed financial decisions.

  • Credit Cards

    Financial institutions can fairly evaluate consumers to provide wider access to credit.

  • Auto Loans

    Indirect and direct auto loan companies can offer competitive terms.

  • Personal Loans

    Lenders can underwrite more consumers without increasing default rates.

  • Buy Now Pay Later

    Providers can reduce their default rate by improving their risk assessment.

Instant connection to consumer-permissioned data

Get easy access to consumer-permissioned bank transaction data through a best-in-class module. With over 95% coverage of financial accounts, consumers can grant access within the digital application journey.

Cleaned, processed, and normalized bank data

Nova Credit has developed proprietary approaches to improve the signal-to-noise ratio of bank transaction data, helping solve for:

  • Differentiating real income from non-income related inflows (e.g. transfers, merchandise returns, etc.)
  • Identifying money movement between accounts owned by same consumer
  • Differentiating between individual and joint accounts and reporting on both the consumer and account levels

Attributes Proven to Predict Risk

Reduce engineering resources needed to quantify risk separation by using a suite of over 1,000 attributes, each performance-tested against actual risk data to ensure predictive power. These attributes are designed to evaluate affordability and credit risk, and are built for underwriting use cases — including adverse action language and special value handling. Each line item is classified as one of four data types — income, assets, debts, and expenses — which are then aggregated into attributes to use in your decisioning and pricing strategies. Attributes represent behaviors over a 7 day to 24 month timeline.

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Results are based on analysis of sample and illustrative data sets and are not guaranteed with respect to any particular end user or use case